Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
This addresses the challenge of maintaining accurate maps for autonomous driving systems, though it is incremental as it builds on existing change detection methods with a new dataset.
The paper tackles the problem of detecting changes between sensor data and high-definition maps for autonomous vehicles, by introducing the TbV dataset and learning-based models that generalize from simulated to real-world data, achieving results on a large-scale dataset with over 7.8 million images.
High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. We present learning-based formulations for solving the problem in the bird's eye view and ego-view. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train our model. Perhaps surprisingly, we show that such models can generalize to real world distributions. The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.8 million images. We make the data available to the public at https://www.argoverse.org/av2.html#mapchange-link, along with code and models at https://github.com/johnwlambert/tbv under the the CC BY-NC-SA 4.0 license.